Intro: At what point in the build season did we find out how accurate our assumptions really were? Was it possible to accelerate this? Could the assumptions be broken down into small experiments? If so, how?
Week Zero was when we measured the accuracy of our assumptions. To accelerate these findings, we could simply run simple, specific scenarios to test the individual components of the robot. Test the acquirement system in landfill, with bins, at the tote chute, running over bumps. Test the lifters with bins, totes, different quantities of totes. Experiment with the cams with sudden jerks, turns, acceleration, deceleration.
Intro: Explain the build-measure-learn feedback loop. What is the purpose of this loop? Why is it a loop?
The build-measure-learn feedback loop is a process in which a product is built quickly, then measured with feedback from others, and from that feedback the designers learn what should be improved or changed. Its purpose is to quicken the building process and make it more effective. It is a loop because there is constant feedback and infinite improvements to make.
Chapter 8: Explain the words "pivot" and "persevere" in the context of a team's way of doing things.
Pivot is to change strategies, realize what isn’t working and find a new way to complete the objective of the game, or to build a new mechanism for the game. Persevere is to continue with whatever method the team has in progress.
Chapter 7: Going back to some of the leap-of-faith assumptions, what would some minimum viable products (MVPs) look like that could validate these assumptions? What would we measure with the MVPs? Think specific to last season's game.
Quick prototypes and CAD drawings are a couple examples of MVP’s. With these, we could measure the effectiveness of a component, how easy it is to fix and assemble, or whether it is a feasible concept in the first place. For example, our rear-loading ramp prototype; with it we measured effectiveness and discussed how to make its use easier.
Chapter 7: Ries talks about metrics with the 3 A's: actionable, accessible, and auditable. Explain what this means and why numbers that don't meet these criteria are vanity metrics.
Statistics and numbers that meet the 3 A’s tend to be more credible. They demonstrate a cause and effect, the data is usable and comprehended by all. Metrics that meet the three A’s are ones to analyze your leap-of-faith assumptions with. Vanity metrics don’t meet one, or multiple, of the criteria. Thus, they can cause false security and aren’t as reliable.
Do not dive into a finished product without small, incremental steps throughout the entirety of the build and design process. Use MVP’s to measure the potential success of a component. Measure your leaps of faith responsibly, don’t fool yourself with false positives. Always get quick feedback and work on that feedback, not what you think they’ll say.